Method for identifying bad pixel against a non-uniform landscape

ABSTRACT

A method for identifying bad pixels in a matrix imaging sensor comprises moving the matrix sensor in a prescribed motion with respect to a background landscape (FIG.  1, 10 ), acquiring a combined image of the landscape, and identifying bad pixels ( 16 ) in the matrix sensor from the combined image. The identification is aided by preferably applying filtering to the combined image. The prescribed motion is preferably rotation of the sensor or translation of the sensor around a circle, and the combined image includes a plurality of preferably similar landscape images. The identification of the bad pixels is performed on-line, in real-time, without requiring the use of a black body.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to matrix detectors, and in particular tothe real-time identification of bad pixels in such detectors against anon-uniform landscape.

Optical systems based on matrix image sensors or “matrix detectors” areknown. Also known are methods of identifying bad or damaged pixels inthese matrices. The identifications normally include a factory two-pointnon-uniformity correction (TPC-NUC). The two-point NUC refers to a testin which a uniform black body image is presented to the detector at twodifferent temperatures. The detector output is measured, and from thesemeasurements one obtains three figures of merit for each picture element(pixel). These three figures of merit are gain (responsivity), level(dark current), and noise. The values of gain, level, and noise shouldbe roughly the same for all the pixels. A pixel that deviatessignificantly from the others in any of these three figures of merit isconsidered (tagged) defective (bad) and included in a “bad pixel” table.

Additionally, after the TPC-NUC, there is sometimes performed anon-line, real-time, continuous one-point correction (OPC) in which onlythe OFFSET is corrected.

This initial tagging of bad or damaged pixels does not assure long-termstable performance of the detector. The matrix may experience during itslifetime deterioration of additional pixels, which need then to be addedto the bad pixel table. In other words, the original bad pixel table isunstable in time. Moreover, even when the deviation of bad pixels from“good” pixels is on the order of the temporal noise, the fact that thedeviation is constant reflects disadvantageously on the systemperformance. Detecting such bad pixels includes the steps of a)positioning a uniform black body in front of a static detector, andsampling of a large number of images; b) obtaining gain level and noisefigures for each pixel, c) identifying bad pixels that deviate from amean value by more that a given criterion; and d) adding the identifieddamaged pixels to the “bad pixel” table. The main problem anddisadvantage of this method lies in the need for a uniform black body.Such a body is normally not available under field conditions, andcertainly not available during normal operation of the system, forexample when the detector is in flight, attached to a body such as amissile.

There is thus a widely recognized need for, and it would be highlyadvantageous to have, an on-line, real-time, simple, and fast method foridentification of bad pixels that appear after the original bad pixeltable is formulated.

SUMMARY OF THE INVENTION

The present invention describes a method for on-line, real-time, simpleand fast identification of bad pixels that appear after an original badpixel table is formulated.

According to the present invention there is provided a method foridentifying defective pixels in a matrix imaging sensor comprising thesteps of moving the matrix sensor in a prescribed motion with respect toa background landscape, acquiring a combined image of the landscape, andidentifying bad pixels in the matrix sensor from the combined image,whereby the identification is performed on-line in real-time withoutnecessitating the use of a black body.

According to one feature in the method of the present invention, thestep of moving includes rotating the matrix sensor.

According to another feature in the method of the present invention, thestep of moving includes translating the matrix sensor in a smooth motionalong a circle.

According to yet another feature in the method of the present invention,the matrix sensor resides in a missile homing head.

According to yet another feature in the method of the present invention,the matrix sensor is fixedly attached to the missile homing head, andthe step of moving the matrix sensor includes moving the missile hominghead.

According to yet another feature in the method of the present invention,the step of identifying bad pixels includes applying a finite impulseresponse filter on the combined image.

According to yet another feature in the method of the present invention,the filtering is performed with a High Pass filter.

According to the present invention there is provided a system foridentifying a bad pixels in a matrix imaging sensor, comprising movingmeans to move the matrix detector in a prescribed motion that enablesthe acquisition of a combined landscape image, and image processingmeans to identify bad pixels in the matrix sensor using the combinedimage.

According to one feature in the system of the present invention, thematrix sensor is located inside a missile homing head, and wherein themoving means include means to rotate the matrix sensor relative to thehoming head.

According to another feature in the system of the present invention, thematrix sensor is fixedly connected to a missile homing head, and whereinthe moving means include means to rotate the homing head.

According to yet another feature in the system of the present invention,the image processing means include a finite impulse response filter forfiltering the combined image.

According to yet another feature in the system of the present invention,the impulse response filter is a high pass filter.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings, wherein:

FIG. 1 shows the principle of the method of the present invention;

FIG. 2 shows in (a) a single image of a landscape, and in (b) a combinedimage that results from the averaging of many rotated landscapes (a);

FIG. 3 shows a filtered combined image with clearly delineated badpixels;

FIG. 4 shows a schematic block diagram of a preferred embodiment systemused to implement the method of the present invention

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is of a method for on-line, real time, simple andfast identification of bad pixels that appear after an original badpixel table is formulated. The principles and operation of the methodaccording to the present invention may be better understood withreference to the drawings and the accompanying description.

FIG. 1 shows the principle of the method. The matrix detector (notshown) viewing a background landscape (or simply “background”) 10 ismoved in a prescribed motion around a center axis 12. The prescribedmotion may be rotation, translation along a circle, etc. Preferably, themotion is simple rotation. If the detector rotation is rapid relative tochanges in the landscape, axis 12 points approximately to the samefeature in the landscape. In a particular exemplary case of a missilehoming head, the detector is typically located in the homing head, androtating the detector is equivalent to rotating the missile head (seesystem description below).

Missile homing heads always have a built-in mechanism for moving thehead in any prescribed motion within some range (called field ofregard), and which can therefore perform the mentioned rotation. Thehoming head may also move in other types of motions, such as atranslational motion along a circle. The necessary conditions for themotion are that it is smooth and continuous, i.e. no jerky motion orstopping are allowed. It should be clear to anyone knowledgeable in theart that any type of motion that fulfils these conditions and rendersthe required results is considered within the scope of the invention.The generic term of “rotation” will be henceforth used to mean all thesetypes of motion.

Each pixel 16 of the matrix rotates around axis 12 on a radius R,covering over a full cycle a “ring” of landscape given by 2πRdR. Thisslowly moving landscape is sampled in a large number of cycles,providing a large number of images that are then overlaid (superposed)to one combined image. Note that because the detector rotation is fastrelative to the movement of the landscape, the set of images forming thecombined image is essentially of the same landscape. As a result, thepoint in the combined image seen by the center matrix element of thedetector relates approximately the same feature in all images of theset. The method works even if the center point is not the same featurein each landscape image. In the combined image, each pixel representsthe average value of that matrix element over the ring. In other words,the pixel assumes the average value of all values covered by the ring.FIG. 2 a shows a single image of a landscape 50, while FIG. 2 b shows acombined image 52, which results from the averaging of many rotatedlandscapes 50. The combined image appears as a series of concentricrings 54 a, b, c, . . . of various gray shades, where the variation inintensity of all pixels in a single ring is no larger than the basicpixel-to-pixel variation. In essence, rotating the scene averages outthe impact of individual landscape bright spots, achieving in effect auniform background, or at least a background uniform along concentricrings, and varying slowly in the radial direction, as clearly seen inthe pictures.

The typical number of images required by the method is on the order of100; however, this number depends on performance: the more images oneuses, the smoother the averaged image comes out, resulting in a smallerprobability of misidentifying a bad pixel. On the other hand, increasingthe number of images increases the time required to complete theprocedure, which is a dead-time as far as normal use of the system isconcerned. The rotation speed should preferably be such that the systemwould undergo several complete rotations during this time.

In FIG. 2 b, one can already see “different” pixels such as a pixel 60that differ significantly from same radius (on the same ring)neighboring pixels. Such different pixels are most preferablydistinguished clearly from other “normal” pixels by applying a finiteimpulse response filter on the combined landscape image to provide afiltered image. Such filtering is preferably done with a high band pass(or simply “High Pass”) or similar filter. In the example shown in FIG.3, image 52 was filtered using a High Pass vertical FIR filter withcoefficients −1, +2, −1. The filtering yielded a filtered image 70 withclearly delineated bad pixels 72 and 74, which were significantlydifferent from the others. Clearly, and within the scope of theinvention, the numerical filter used may be different from the onespecified above (with coefficients −1, +2, −1 along a row or column). Asmentioned, this filter is preferably a High Pass filter in order toaccentuate the deviation of the different pixel from its neighbors,while not being affected from slow variations perpendicular to therings. It is well known that High Pass filters may be realized in manyways, as pointed out in standard engineering textbooks, and all thevarious ways of implementing them are considered within the scope of thepresent invention.

In the exemplary case of a missile that always includes known imageacquisition means, image processing means, and computing means, the dataacquisition and processing mentioned above are preferably done withthese already existing means.

The distributions of both the level and the noise, after applying theHigh Pass filter, closely follow Gaussian statistics (i.e. normal,bell-shaped, etc.). Detecting deviations from a normal distribution is astandard textbook procedure. One typically starts by finding the averageand standard deviation of the population, then proceeds by calling apixel defective if the pixel value deviates from the average by morethan a certain number of standard deviations. This ‘certain number’(typically 3-5) depends on the penalty on making each of the two typesof errors: error of omission or error of commission, i.e. designating abad pixel as good, or designating a good pixel as bad. This is anengineering problem with well-known solutions, depending on therequirements from the particular system at hand.

In terms of a system that can be used to perform the on-line, real-time,simple and fast method for identification of bad pixels according to thepresent invention, any system that can acquire, through a rotatingmatrix detector, an overlaid combined image of a landscape, process thatimage, and apply the preferred filtering, falls within the scope of thepresent invention. A schematic description of a preferred embodiment ofsuch a system, as applied for example in a homing head of a missile, isshown in FIG. 4. A homing seeker head 100 comprises a matrix detector102 that has a plurality of pixels, potentially among them one or morebad pixels as defined above. Matrix detector 102, rotated by rotatingmeans 104, rotates around an axis 106, while looking at a landscape 108.The rotation of the detector is fast relative to changes in thelandscape. For example, if the detector resides in a missile hominghead, and the missile is fixedly attached to a maneuvering aircraft, theturns and rolls of the aircraft cause the missile length axis to pointat a changing landscape. However, if the detector rotation is muchfaster than the relative movement of the aircraft vs the landscape (e.g.the landscape is far, and the detector acquisition time for the combinedimage is very short) the detector looks essentially at the samelandscape during the image acquisition. Clearly, detector 102 may befixedly attached to homing head 100, in which case the rotating meansrotates the homing head itself, as explained above. Alternatively,detector 102 may be flexibly attached to the homing head, drivenindependently of the homing head by means 104. Homing head 100 furthercomprises image processing means 112, which typically include amicroprocessor 114, a storage means 116, and filtering means 118,preferably a High Pass filter. The information acquired by detector 102is transferred to image processing means 112 by a fixed electricalharness 120 and trough a slip ring assembly 122 (that allows arotational movement between both parts). Alternatively, the informationmay be transferred wirelessly from a transmitter attached to detector102 to a receiver attached to means 112. Microprocessor 114, storagemeans 116 and filtering means 118 cooperatively process the informationby well-known image processing techniques.

In summary, the present invention provides an on-line, real-time, simpleand fast method for identification of bad pixels that appear after theoriginal bad pixel table is formulated. In contrast with existingcompeting methods, the method of the present invention does not requirea uniform black body, and is therefore particularly advantageous anduseful in field conditions, e.g. for missiles in flight.

While the invention has been described with respect to a limited numberof embodiments, it will be appreciated that many variations,modifications and other applications of the invention may be made.

What is claimed is:
 1. A method for identifying defective pixels in amatrix imaging sensor comprising the steps of: a. moving the matrixsensor in a prescribed motion with respect to a background landscape; b.acquiring a combined image of the landscape; and c. identifying badpixels in the matrix sensor from the combined image; whereby theidentification is performed on-line in real-time without necessitatingthe use of a black body.
 2. The method of claim 1, wherein the step ofmoving includes rotating the matrix sensor.
 3. The method of claim 1,wherein the step of moving includes translating the matrix sensor in asmooth motion along a circle.
 4. The method of claim 1, wherein thematrix sensor resides in a missile homing head.
 5. The method of claim4, wherein the matrix sensor is fixedly attached to the missile hominghead, and wherein the step of moving the matrix sensor includes movingthe missile homing head.
 6. The method of claim 1, wherein the step ofidentifying bad pixels includes applying a finite impulse responsefilter on the combined image.
 7. The method of claim 6, wherein thefiltering is performed with a High Pass filter.
 8. A system foridentifying a bad pixels in a matrix imaging sensor, comprising: a.moving means to move the matrix detector in a prescribed motion thatenables the acquisition of a combined landscape image; and b. imageprocessing means to identify bad pixels of the matrix sensor using thecombined image.
 9. The system of claim 8, wherein the matrix sensor islocated inside a missile homing head, and wherein the moving meansinclude means to rotate the matrix sensor relative to the homing head.10. The system of claim 8, wherein the matrix sensor is fixedlyconnected to a missile homing head, and wherein the moving means includemeans to rotate the homing head.
 11. The system of claim 8, wherein theimage processing means include a finite impulse response filter forfiltering the combined image.
 12. The system of claim 11, wherein thefinite impulse response filter is a high pass filter.